Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity. OED grading is subject to large inter/intra-rater variability, resulting in the under/over-treatment of patients. We developed a new Transformer-based pipeline to improve detection and segmentation of OED in haematoxylin and eosin (H&E) stained whole slide images (WSIs). Our model was trained on OED cases (n = 260) and controls (n = 105) collected using three different scanners, and validated on test data from three external centres in the United Kingdom and Brazil (n = 78). Our internal experiments yield a mean F1-score of 0.81 for OED segmentation, which reduced slightly to 0.71 on external testing, showing good generalisability, and gaining state-of-the-art results. This is the first externally validated study to use Transformers for segmentation in precancerous histology images. Our publicly available model shows great promise to be the first step of a fully-integrated pipeline, allowing earlier and more efficient OED diagnosis, ultimately benefiting patient outcomes.

翻译：口腔上皮异型增生（OED）是一种口腔病变的癌前组织病理学诊断。OED分级存在较大的观察者间/观察者内变异，导致患者治疗不足或过度治疗。我们开发了一种新的基于Transformer的流水线，用于改善苏木精-伊红（H&E）染色的全切片图像（WSI）中OED的检测与分割。该模型基于使用三种不同扫描仪收集的OED病例（n=260）和对照组（n=105）进行训练，并在来自英国和巴西三个外部中心的测试数据（n=78）上进行验证。内部实验结果显示，OED分割的平均F1分数为0.81，在外部测试中略微下降至0.71，展现出良好的泛化能力，并取得了最先进的性能。这是首个采用Transformer进行癌前组织学图像分割并经过外部验证的研究。我们公开的模型有望成为全集成流水线的第一步，实现更早期、更高效的OED诊断，最终改善患者预后。